From 3ca50553ee3f9c1b45666ab025bb7450ef4881e7 Mon Sep 17 00:00:00 2001
From: Xi Xu <i@xi-xu.me>
Date: Thu, 16 May 2024 09:46:25 +0800
Subject: [PATCH] Add 7

---
 7/1.py | 34 ++++++++++++++++++++++++++++++++++
 7/2.py | 53 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 7/3.py | 41 +++++++++++++++++++++++++++++++++++++++++
 3 files changed, 128 insertions(+)
 create mode 100644 7/1.py
 create mode 100644 7/2.py
 create mode 100644 7/3.py

diff --git a/7/1.py b/7/1.py
new file mode 100644
index 0000000..bd6a5ee
--- /dev/null
+++ b/7/1.py
@@ -0,0 +1,34 @@
+import cv2 as cv
+import numpy as np
+import matplotlib.pyplot as plt
+
+plt.rcParams["font.sans-serif"] = ["SimSun"]
+
+img = cv.imread(r"img\paopao.jpg", cv.IMREAD_GRAYSCALE)
+G1 = np.zeros(img.shape, np.uint8)
+G2 = np.zeros(img.shape, np.uint8)
+T1 = np.mean(img)  # type: ignore
+diff = 255
+T0 = 0.01
+while diff > T0:
+    _, G1 = cv.threshold(img, T1, 255, cv.THRESH_TOZERO_INV)
+    _, G2 = cv.threshold(img, T1, 255, cv.THRESH_TOZERO)
+    loc1 = np.where(G1 > 0.001)  # type: ignore
+    loc2 = np.where(G2 > 0.001)  # type: ignore
+    ave1 = np.mean(G1[loc1])  # type: ignore
+    ave2 = np.mean(G2[loc2])  # type: ignore
+    T2 = (ave1 + ave2) / 2
+    diff = np.abs(T1 - T2)
+    T1 = T2
+_, img_result = cv.threshold(img, T1, 255, cv.THRESH_BINARY)
+plt.figure()
+plt.subplot(121)
+plt.axis("off")
+plt.imshow(img, cmap="gray")
+plt.title("原灰度图像")
+plt.subplot(122)
+plt.axis("off")
+plt.imshow(img_result, cmap="gray")
+plt.title("迭代全阈值分割二值图像")
+plt.tight_layout()
+plt.show()
diff --git a/7/2.py b/7/2.py
new file mode 100644
index 0000000..839809b
--- /dev/null
+++ b/7/2.py
@@ -0,0 +1,53 @@
+import cv2 as cv
+import numpy as np
+import matplotlib.pyplot as plt
+
+plt.rcParams["font.sans-serif"] = ["SimSun"]
+
+
+def histogram(img):
+    row, col = img.shape
+    hist = [0] * 256
+    for i in range(row):
+        for j in range(col):
+            hist[img[i, j]] += 1
+    return hist
+
+
+if __name__ == "__main__":
+    img = cv.imread(r"img\polygon_draw.jpg", cv.IMREAD_GRAYSCALE)
+    img_noisy = np.uint8(img + 0.8 * img.std() * np.random.standard_normal(img.shape))
+    img_noisy_blur = cv.GaussianBlur(img_noisy, (9, 9), 0)  # type: ignore
+    _, img_result = cv.threshold(img_noisy, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)  # type: ignore
+    _, img_result_blur = cv.threshold(
+        img_noisy_blur, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU
+    )
+    plt.figure()
+    plt.subplot(231)
+    plt.axis("off")
+    plt.imshow(img_noisy, cmap="gray")
+    plt.title("带噪声的图像")
+    plt.subplot(232)
+    plt.xlabel("灰度值")
+    plt.ylabel("像素个数")
+    plt.bar(range(256), histogram(img_noisy))
+    plt.title("噪声图像直方图")
+    plt.subplot(233)
+    plt.axis("off")
+    plt.imshow(img_result, cmap="gray")
+    plt.title("带噪声图像的 OTSU 分割")
+    plt.subplot(234)
+    plt.axis("off")
+    plt.imshow(img_noisy_blur, cmap="gray")
+    plt.title("高斯平滑的图像")
+    plt.subplot(235)
+    plt.xlabel("灰度值")
+    plt.ylabel("像素个数")
+    plt.bar(range(256), histogram(img_noisy_blur))
+    plt.title("平滑图像直方图")
+    plt.subplot(236)
+    plt.axis("off")
+    plt.imshow(img_result_blur, cmap="gray")
+    plt.title("平滑图像的 OTSU 分割")
+    plt.tight_layout()
+    plt.show()
diff --git a/7/3.py b/7/3.py
new file mode 100644
index 0000000..c24f292
--- /dev/null
+++ b/7/3.py
@@ -0,0 +1,41 @@
+import cv2 as cv
+import matplotlib.pyplot as plt
+
+plt.rcParams["font.sans-serif"] = ["SimSun"]
+
+img = cv.imread(r"img\light_circle.jpg", cv.IMREAD_GRAYSCALE)
+kernalSize = 19
+img_adapt = cv.adaptiveThreshold(
+    img, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, kernalSize, 6
+)
+img_tophat = cv.morphologyEx(
+    img, cv.MORPH_TOPHAT, cv.getStructuringElement(cv.MORPH_ELLIPSE, (45, 45))
+)
+plt.figure()
+plt.subplot(121)
+plt.imshow(img, cmap="gray")
+plt.title("待处理灰度图像")
+plt.axis("off")
+plt.subplot(122)
+plt.imshow(img_adapt, cmap="gray")
+plt.title("自适应阈值分割结果")
+plt.axis("off")
+plt.tight_layout()
+plt.show()
+
+# # 交互式调整参数
+# kernalSize = 7
+# plt.ion()
+# for i in range(10):
+#     c = 4
+#     for j in range(10):
+#         plt.cla()
+#         img_seg_adapt = cv.adaptiveThreshold(
+#             img0, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, kernalSize, c
+#         )
+#         plt.imshow(img_seg_adapt, cmap="gray")
+#         print(kernalSize, c)
+#         plt.pause(0.01)
+#         c += 2
+#     kernalSize += 2
+# plt.show()